Description Arguments Details Value Methods Author(s) References See Also
Peak density and wavelet based feature detection for high resolution LC/MS data in centroid mode with additional peak picking of isotope features on basis of isotope peak predictions
object |
|
ppm |
maxmial tolerated m/z deviation in consecutive scans, in ppm (parts per million) |
peakwidth |
Chromatographic peak width, given as range (min,max) in seconds |
snthresh |
signal to noise ratio cutoff, definition see below. |
prefilter |
|
mzCenterFun |
Function to calculate the m/z center of the feature: |
integrate |
Integration method. If |
mzdiff |
minimum difference in m/z for peaks with overlapping retention times, can be negative to allow overlap |
fitgauss |
logical, if TRUE a Gaussian is fitted to each peak |
scanrange |
scan range to process |
noise |
optional argument which is useful for data that was centroided without any intensity threshold,
centroids with intensity < |
sleep |
number of seconds to pause between plotting peak finding cycles |
verbose.columns |
logical, if TRUE additional peak meta data columns are returned |
ROI.list |
A optional list of ROIs that represents detected mass traces (ROIs). If this list is empty (default) then centWave detects the mass trace ROIs,
otherwise this step is skipped and the supplied ROIs are used in the peak detection phase. Each ROI object in the list has the following slots:
|
firstBaselineCheck |
logical, if TRUE continuous data within ROI is checked to be above 1st baseline |
roiScales |
numeric, optional vector of scales for each ROI in |
snthreshIsoROIs |
signal to noise ratio cutoff for predicted isotope ROIs, definition see below. |
maxcharge |
max. number of the isotope charge. |
maxiso |
max. number of the isotope peaks to predict for each detected feature. |
mzIntervalExtension |
logical, if TRUE predicted isotope ROIs (regions of interest) are extended in the m/z dimension to increase the detection of low intensity and hence noisy peaks. |
This algorithm is most suitable for high resolution LC/{TOF,OrbiTrap,FTICR}-MS data in centroid mode.
The centWave
algorithm is applied in two peak picking steps as follows. In the first peak picking step ROIs (regions of interest, characterised as regions with less than ppm
m/z deviation in consecutive scans) in the LC/MS map are located and further analysed using continuous wavelet transform (CWT) for the localization of chromatographic peaks on different scales.
In the second peak picking step isotope ROIs in the LC/MS map are predicted further analysed using continuous wavelet transform (CWT) for the localization of chromatographic peaks on different scales.
The peak lists resulting from both peak picking steps are merged and redundant peaks are removed.
A matrix with columns:
mz |
weighted (by intensity) mean of peak m/z across scans |
mzmin |
m/z peak minimum |
mzmax |
m/z peak maximum |
rt |
retention time of peak midpoint |
rtmin |
leading edge of peak retention time |
rtmax |
trailing edge of peak retention time |
into |
integrated peak intensity |
intb |
baseline corrected integrated peak intensity |
maxo |
maximum peak intensity |
sn |
Signal/Noise ratio, defined as |
egauss |
RMSE of Gaussian fit |
|
if |
mu |
Gaussian parameter mu |
sigma |
Gaussian parameter sigma |
h |
Gaussian parameter h |
f |
Region number of m/z ROI where the peak was localised |
dppm |
m/z deviation of mass trace across scans in ppm |
scale |
Scale on which the peak was localised |
scpos |
Peak position found by wavelet analysis |
scmin |
Left peak limit found by wavelet analysis (scan number) |
scmax |
Right peak limit found by wavelet analysis (scan number) |
findPeaks.centWaveWithPredictedIsotopeROIs(object, ppm=25, peakwidth=c(20,50), snthresh=10,
prefilter=c(3,100), mzCenterFun="wMean", integrate=1, mzdiff=-0.001, fitgauss=FALSE,
scanrange= numeric(), noise=0, sleep=0, verbose.columns=FALSE, ROI.list=list(),
firstBaselineCheck=TRUE, roiScales=NULL, snthreshIsoROIs=6.25, maxcharge=3, maxiso=5, mzIntervalExtension=TRUE)
Ralf Tautenhahn
Ralf Tautenhahn, Christoph B\"ottcher, and Steffen Neumann "Highly sensitive feature detection for high resolution LC/MS" BMC Bioinformatics 2008, 9:504\ Hendrik Treutler and Steffen Neumann. "Prediction, detection, and validation of isotope clusters in mass spectrometry data" Submitted to Metabolites 2016, Special Issue "Bioinformatics and Data Analysis"
do_findChromPeaks_centWaveWithPredIsoROIs
for the
corresponding core API function.
findPeaks.addPredictedIsotopeFeatures
findPeaks.centWave
findPeaks-methods
xcmsRaw-class
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